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Ad Hoc Networks. 8th International Conference, ADHOCNETS 2016, Ottawa, Canada, September 26-27, 2016, Revised Selected Papers

Research Article

Relative Localization for Small Wireless Sensor Networks

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  • @INPROCEEDINGS{10.1007/978-3-319-51204-4_2,
        author={Yifeng Zhou and Franklin Wong},
        title={Relative Localization for Small Wireless Sensor Networks},
        proceedings={Ad Hoc Networks. 8th International Conference, ADHOCNETS 2016, Ottawa, Canada, September 26-27, 2016, Revised Selected Papers},
        proceedings_a={ADHOCNETS},
        year={2017},
        month={4},
        keywords={Wireless sensor networks Localization Ranging Ultra-wide band (UWB) Least squares (LS) Maximum likelihood method (MLE) Multidimensional scaling (MDS)},
        doi={10.1007/978-3-319-51204-4_2}
    }
    
  • Yifeng Zhou
    Franklin Wong
    Year: 2017
    Relative Localization for Small Wireless Sensor Networks
    ADHOCNETS
    Springer
    DOI: 10.1007/978-3-319-51204-4_2
Yifeng Zhou1,*, Franklin Wong2,*
  • 1: Communications Research Centre Canada
  • 2: Defence Research and Development Canada
*Contact email: yifeng.zhou@canada.ca, franklin.wong@drdc-rddc.gc.ca

Abstract

In this paper, we investigate relative localization techniques based on internode distance measurements for small wireless networks. High precision ranging is assumed, which is achieved by using technologies such as ultra-wide band (UWB) ranging. A number of approaches are formulated and compared for relative location estimation, which include the Linear Least Squares (LLS) approach, the Maximum Likelihood Estimation (MLE) approach, the Map Registration Approach (MAP), the Multidimensional Scaling (MDS) approach and the enhanced MDS approaches. Finally, computer simulations are used to compare the performances and effectiveness of these techniques, and conclusions are drawn on the suitability of the relative localization techniques for small networks.

Keywords
Wireless sensor networks Localization Ranging Ultra-wide band (UWB) Least squares (LS) Maximum likelihood method (MLE) Multidimensional scaling (MDS)
Published
2017-04-24
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-319-51204-4_2
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